Open Source Python Software
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Python Software
Browse free open source Python Software and projects below. Use the toggles on the left to filter open source Python Software by OS, license, language, programming language, and project status.
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nmon for Linux
Linux performance monitoring on-screen or to CSV file
nmon is short for Nigel's Performance Monitor It either shows you the stats on-screen updating once a second or saves the data to a CSV file for later analysis and graphing. For details see the homepage ==> http://nmon.sourceforge.net Please use the latest version. The new version "njmon" outputs JSON format that a lot of tools use these days. It integrates well with saving data directly to InfluxDB and graphing with Grafana. -
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Memobase
Fast backend for long-term AI user memory via structured profiles
Memobase is an open source backend system that enables long-term user memory functionality for AI applications by capturing and structuring information about users across interactions. Its design centers on creating user profiles and recording event timelines, allowing AI systems to remember, understand, and evolve in their behaviour toward individual users over time. Instead of relying purely on traditional embedding-based retrieval or RAG systems, Memobase uses profile and timeline structures to deliver memory that reflects user context efficiently and meaningfully. The system focuses on three principal performance metrics: high search performance, reduced large language model (LLM) costs through batch processing techniques, and low latency with minimal SQL operations. Memobase supports integration with existing LLM workflows via APIs and SDKs (including Python, Node, and Go), making it easy to adopt within diverse application stacks. -
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FuzzBench
FuzzBench - Fuzzer benchmarking as a service
FuzzBench is a large-scale, open research platform developed by Google to evaluate and benchmark fuzzers — automated software testing tools that detect vulnerabilities through randomized input generation. It provides a standardized, reproducible environment for comparing the performance and effectiveness of different fuzzing algorithms on real-world software targets. FuzzBench integrates with the OSS-Fuzz infrastructure, allowing it to run experiments on authentic open source projects and collect meaningful data on crash discovery rates, code coverage, and bug-finding efficiency. The service includes an easy-to-use API for integrating custom fuzzers and an automated reporting system that generates detailed statistical analyses, comparative graphs, and significance testing. By running experiments at Google scale, FuzzBench ensures consistent, unbiased, and data-driven evaluations that support academic and industrial fuzzing research. -
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Shaderc
A collection of tools, libraries, and tests for Vulkan shader
Shaderc is a collection of tools and libraries for compiling shaders—small programs that run on GPUs—into SPIR-V, the intermediate representation used by the Vulkan graphics API. It provides both a command-line tool (glslc) and a C/C++ library (libshaderc) that wrap the functionality of glslang (the Khronos reference compiler for GLSL) and SPIRV-Tools to deliver a modern, scriptable, and efficient shader compilation workflow. The glslc compiler offers a GCC/Clang-like interface for building GLSL and HLSL shaders, making it easy to integrate into existing build systems. Meanwhile, libshaderc exposes a stable API that allows developers to programmatically compile shader strings into SPIR-V modules within graphics engines and tools. Shaderc supports advanced features such as file inclusion (#include), concurrency, and cross-platform builds, and it maintains backward compatibility for long-term projects. -
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Mozc
Mozc - a Japanese Input Method Editor designed for multi-platform
Mozc is an open source Japanese Input Method Editor (IME) developed by Google, designed to provide Japanese text input across multiple operating systems including Android, macOS, Windows, GNU/Linux, and Chromium OS. The project originated as a subset of Google Japanese Input, released publicly under the BSD 3-Clause license for community use and development. Mozc offers core IME functionality such as text conversion, prediction, and dictionary-based input, enabling users to efficiently type and edit Japanese text. While Mozc shares much of its codebase with Google’s internal IME, it operates as an independent open source project without official support, guarantees, or stable release cycles. Developers can build Mozc from source for their preferred platform, and the repository includes detailed build instructions for Android, Linux, macOS, and Windows environments. -
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Souper
A superoptimizer for LLVM IR
Souper is a superoptimizer for LLVM IR designed to automatically discover missed optimization opportunities within LLVM’s mid-end optimizers. By leveraging SMT (Satisfiability Modulo Theories) solvers, Souper symbolically analyzes LLVM Intermediate Representation (IR) to synthesize equivalent, more efficient expressions. It identifies peephole optimizations—small instruction-level improvements—that LLVM’s optimizer may overlook, thereby improving compiled code quality. Souper can operate in multiple modes: as a standalone analysis tool that extracts and solves SMT queries from LLVM bitcode, or as an LLVM optimization pass that applies discovered optimizations directly to the IR. It integrates seamlessly with Clang and LLVM tools and supports caching mechanisms (RAM or Redis) to accelerate repeated analyses. The system can output suggested rewrites for developers or automatically apply transformations, helping compiler engineers improve optimization passes. -
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Menagerie
A collection of high-quality models for the MuJoCo physics engine
MuJoCo Menagerie, developed by Google DeepMind, is a curated collection of high-quality simulation models designed for use with the MuJoCo physics engine. It serves as a comprehensive library of accurate and ready-to-use robotic, biomechanical, and mechanical models, ensuring users can perform reliable simulations without having to build or tune models from scratch. The repository aims to improve reproducibility and quality across robotics research by providing verified models that adhere to consistent design and physical standards. Each model directory contains its 3D assets, MJCF XML definitions, licensing information, and example scenes for visualization and testing. The collection spans a wide range of categories including robotic arms, humanoids, quadrupeds, mobile manipulators, drones, and biomechanical systems. Users can access models directly via the robot_descriptions Python package or by cloning the repository for use in interactive MuJoCo simulations. -
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Mathematics Dataset
This dataset code generates mathematical question and answer pairs
The Mathematics Dataset, developed by Google DeepMind, is a synthetic dataset designed to evaluate and train machine learning models on mathematical reasoning and symbolic manipulation. It generates question-and-answer pairs across a wide range of mathematical topics typically found in school-level curricula, testing a model’s ability to reason about algebra, arithmetic, calculus, probability, and more. Each question is programmatically generated with structured templates to ensure clear logic and reproducibility. The dataset enables models to learn mathematical problem-solving through examples that involve both numeric and symbolic reasoning. Version 1.0 includes over 2 million examples per category, with training splits labeled as “easy,” “medium,” and “hard,” supporting curriculum-based learning strategies. The data can be accessed via PyPI or generated locally using provided Python scripts, with outputs formatted for direct use in training or evaluation pipelines. -
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Sherloq
An open source digital image forensic toolset
Sherloq is a research-oriented toolkit designed for digital image forensics, providing an integrated environment to experiment with algorithms for image analysis and tampering detection. Rather than functioning as an automated decision-making system, it serves as a companion tool for researchers, enthusiasts, and students who want to explore forensic techniques from scientific literature and workshops. The project emphasizes transparency and community collaboration, contrasting with proprietary forensic tools that often rely on secrecy. Initially developed in C++ in 2015 and later transitioned to a Qt-based GUI in 2017, Sherloq has since been ported to Python with PySide2, Matplotlib, and OpenCV to improve accessibility and ease of development. Its interface allows users to inspect images with real-time zoom, metadata exploration, noise analysis, and specialized algorithms for detecting forgeries and manipulations. -
Easy-to-use Business Software for the Waste Management Software IndustryDOP Software’s mission is to streamline waste and recycling business’ processes by providing them with dynamic, comprehensive software and services that increase productivity and quality of performance.
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openSkyMatch
Matches OpenScience Observatories images with astronomical catalogs
openSkyMatch is a collection of Linux shell and Python scripts designed for the OpenScience Observatories program. It automates the identification and matching of detected celestial objects in locally captured FITS images with entries in large-scale sky catalogs, notably Pan-STARRS1 DR2 (II/389/ps1_dr2). The toolkit supports data preprocessing, coordinate correlation, and catalog-based validation of astronomical detections. All tools are open-source and optimized for reproducibility and transparency in citizen science astronomy. -
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gestorProcessos
Gestiona l'execució de processos en diferents servidors i entorns.
Gestiona l'execució de processos, procediments, instruccions en diferents màquines i diferents entorns, de manera interactiva o programada, els resultats de les execucions poden ser enviades per correu electrònic. Es realitza la gestió de maquinari, servidors, usuaris i grups per donar autorització a l'execució de processos o procediments o instruccions. -
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Insight Toolkit (ITK). ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions. New location: https://github.com/InsightSoftwareConsortium/ITK
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Digraph3
A collection of python3 modules for Algorithmic Decision Theory
This collection of Python3 modules provides a large range of implemented decision aiding algorithms useful in the field of outranking digraphs based Multiple Criteria Decision Aid (MCDA), especially best choice, linear ranking and absolute or relative rating algorithms with multiple incommensurable criteria. Technical documentation and tutorials are available under the following link: https://digraph3.readthedocs.io/en/latest/ The tutorials introduce the main objects like digraphs, outranking digraphs and performance tableaux. There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank or rate with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing maximal independent sets (MISs) and kernels in graphs and digraphs. -
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dreamlux-cli
Dreamlux CLI is an open-source command-line tool for generating AI-pow
Dreamlux CLI is a developer-friendly command-line tool for AI video generation. It allows developers to generate videos from text prompts and images using the Dreamlux API. Key features: - AI-powered text-to-video and image-to-video generation - Easy-to-use CLI interface - Designed for automation and developer workflows - Integrates with the Dreamlux platform Project website: https://dreamlux.ai -
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MinerU
A high-quality tool for convert PDF to Markdown and JSON
MinerU is an open-source, high-quality document extraction toolkit focused on converting PDFs (and other document formats) into structured Markdown and JSON. It leverages OCR and layout analysis to preserve semantic structure and metadata, ideal for research and data science workflows. -
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Marvin
A batteries-included library for building AI-powered software
Meet Marvin: a batteries-included library for building AI-powered software. Marvin's job is to integrate AI directly into your codebase by making it look and feel like any other function. Marvin introduces a new concept called AI Functions. These functions differ from conventional ones in that they don’t rely on source code, but instead generate their outputs on-demand through AI. With AI functions, you don't have to write complex code for tasks like extracting entities from web pages, scoring sentiment, or categorizing items in your database. Just describe your needs, call the function, and you're done. AI functions work with native data types, so you can seamlessly integrate them into any codebase and chain them into sophisticated pipelines. In addition to AI functions, Marvin also introduces more flexible bots. Bots are highly capable AI assistants that can be given specific instructions and personalities or roles. -
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Crossfire RPG game
Cooperative multiplayer graphical RPG and adventure game
Crossfire is a free, open-source, cooperative multi-player RPG and adventure game. Since its initial release, Crossfire has grown to encompass over 150 monsters, 3000 areas to explore, an elaborate magic system, 14 races, 16 character classes, and many powerful artifacts scattered far and wide. Set in a fantastical medieval world, it blends the style of Gauntlet, NetHack, Moria, and Angband. -
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KoboldCpp
Run GGUF models easily with a UI or API. One File. Zero Install.
KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features. -
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Acontext
Context data platform for building observable, self-learning AI agents
Acontext is a cloud-native context data platform designed to support the development and operation of advanced AI agents. It provides a unified system to store and manage contexts, multimodal messages, artifacts, and task workflows, enabling developers to engineer context effectively for their agent products. The platform observes agent tasks and user feedback in real time, offering robust observability into workflows and helping teams understand how agents perform over time. Acontext also supports agent self-learning by distilling structured skills and experiences from previously completed tasks, which can later be reused or searched to improve future performance. It includes tools to interact with session data, background agents that monitor progress, and a dashboard that visualizes success rates, artifacts, and learned skills. By combining persistent storage, observability, and learning capabilities, Acontext aims to make AI agents more scalable, reliable, and capable. -
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OpenRLHF
An Easy-to-use, Scalable and High-performance RLHF Framework
OpenRLHF is an easy-to-use, scalable, and high-performance framework for Reinforcement Learning with Human Feedback (RLHF). It supports various training techniques and model architectures. -
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Klavis AI
MCP integration platforms for AI agents to use tools at any scale
Klavis AI is a Y Combinator X25-backed open-source infrastructure platform that enables AI agents to reliably connect with external tools and services at scale through Model Context Protocol (MCP). Founded by ex-Google DeepMind and ex-Lyft engineers, Klavis provides 50+ production-ready MCP servers with enterprise OAuth support for GitHub, Slack, Gmail, Salesforce, Linear, Notion, and more. The flagship product Strata solves tool overload through progressive discovery, achieving +13% higher accuracy and 83%+ success on complex workflows. Developers can integrate via Python/TypeScript SDKs or REST API, with support for OpenAI, Claude, Gemini, LangChain, LlamaIndex, and CrewAI. Features include built-in authentication, multi-tenancy, hosted servers, Docker support, and enterprise security guardrails. Licensed under Apache 2.0, Klavis simplifies AI development by eliminating complex authentication management and enabling seamless workflow automation across multiple applications. -
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YAPF
A formatter for Python files
YAPF is a Python code formatter that automatically rewrites source to match a chosen style, using a clang-format–inspired algorithm to search for the “best” layout under your rules. Instead of relying on a fixed set of heuristics, it explores formatting decisions and chooses the lowest-cost result, aiming to produce code a human would write when following a style guide. You can run it as a command-line tool or call it as a library via FormatCode / FormatFile, making it easy to embed in editors, CI, and custom tooling. Styles are highly configurable: start from presets like pep8, google, yapf, or facebook, then override dozens of options in .style.yapf, setup.cfg, or pyproject.toml. It supports recursive directory formatting, line-range formatting, and diff-only output so you can check or fix just the lines you touched. -
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Database connection pool with support for lots of languages and databases.
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InvokeAI
InvokeAI is a leading creative engine for Stable Diffusion models
InvokeAI is an implementation of Stable Diffusion, the open source text-to-image and image-to-image generator. It provides a streamlined process with various new features and options to aid the image generation process. It runs on Windows, Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM. InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products. This fork is supported across Linux, Windows and Macintosh. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver). We do not recommend the GTX 1650 or 1660 series video cards. They are unable to run in half-precision mode and do not have sufficient VRAM to render 512x512 images. -
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MemU
MemU is an open-source memory framework for AI companions
MemU is an agentic memory layer for LLM applications, specifically designed for AI companions. Transform your memory into an intelligent file system that automatically organizes, connects, and evolves with your memories. Simple, fast, and reliable memory infrastructure for AI applications. Powerful tools and dedicated support to scale your AI applications with confidence. Full proprietary features, commercial usage rights, and white-labeling options for your enterprise needs. SSO/RBAC integration and a dedicated algorithm team for scenario-specific optimization. User behavior analysis, real-time monitoring, and automated agent optimization tools. 24/7 dedicated support team, custom SLAs, and professional implementation services.
